Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provide...

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Autores principales:	Chamay Kruger, Willem Daniel Schutte, Tanja Verster
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	representativeness regulation LGD model performance Global Credit Data (GCD) pooled data Insurance HG8011-9999
Acceso en línea:	https://doaj.org/article/bbbf6ba1bd0a4c899f6dbab8ebe0103b
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spelling	oai:doaj.org-article:bbbf6ba1bd0a4c899f6dbab8ebe0103b2021-11-25T18:56:12ZUsing Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions10.3390/risks91102042227-9091https://doaj.org/article/bbbf6ba1bd0a4c899f6dbab8ebe0103b2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9091/9/11/204https://doaj.org/toc/2227-9091This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.Chamay KrugerWillem Daniel SchutteTanja VersterMDPI AGarticlerepresentativenessregulationLGDmodel performanceGlobal Credit Data (GCD)pooled dataInsuranceHG8011-9999ENRisks, Vol 9, Iss 204, p 204 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	representativeness regulation LGD model performance Global Credit Data (GCD) pooled data Insurance HG8011-9999
spellingShingle	representativeness regulation LGD model performance Global Credit Data (GCD) pooled data Insurance HG8011-9999 Chamay Kruger Willem Daniel Schutte Tanja Verster Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
description	This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.
format	article
author	Chamay Kruger Willem Daniel Schutte Tanja Verster
author_facet	Chamay Kruger Willem Daniel Schutte Tanja Verster
author_sort	Chamay Kruger
title	Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
title_short	Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
title_full	Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
title_fullStr	Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
title_full_unstemmed	Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
title_sort	using model performance to assess the representativeness of data for model development and calibration in financial institutions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bbbf6ba1bd0a4c899f6dbab8ebe0103b
work_keys_str_mv	AT chamaykruger usingmodelperformancetoassesstherepresentativenessofdataformodeldevelopmentandcalibrationinfinancialinstitutions AT willemdanielschutte usingmodelperformancetoassesstherepresentativenessofdataformodeldevelopmentandcalibrationinfinancialinstitutions AT tanjaverster usingmodelperformancetoassesstherepresentativenessofdataformodeldevelopmentandcalibrationinfinancialinstitutions
_version_	1718410549498216448

Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

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